Chips (Feb 2023)

An Interface Platform for Robotic Neuromorphic Systems

  • Nicola Russo,
  • Haochun Huang,
  • Eugenio Donati,
  • Thomas Madsen,
  • Konstantin Nikolic

DOI
https://doi.org/10.3390/chips2010002
Journal volume & issue
Vol. 2, no. 1
pp. 20 – 30

Abstract

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Neuromorphic computing is promising to become a future standard in low-power AI applications. The integration between new neuromorphic hardware and traditional microcontrollers is an open challenge. In this paper, we present an interface board and a communication protocol that allows communication between different devices, using a microcontroller unit (Arduino Due) in the middle. Our compact printed circuit board (PCB) links different devices as a whole system and provides a power supply for the entire system using batteries as the power supply. Concretely, we have connected a Dynamic Vision Sensor (DVS128), SpiNNaker board and a servo motor, creating a platform for a neuromorphic robotic system controlled by a Spiking Neural Network, which is demonstrated on the task of intercepting incoming objects. The data rate of the implemented interface board is 24.64 k symbols/s and the latency for generating commands is about 11ms. The complete system is run only by batteries, making it very suitable for robotic applications.

Keywords